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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Yuan Gallagher, M. |
| Copyright Year | 2003 |
| Description | Author affiliation: Sch. of Inf. Technol. & Electr. Eng., Queensland Univ., Brisbane, Qld., Australia (Bo Yuan; Gallagher, M.) |
| Abstract | As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms. |
| Sponsorship | Air Force Iffice of Sci. Res. Asian Office of Aerospace Res and Development Army Res. Office - Far East CISCO Evoluationary Programming Soc. IEE Inst. of Engineers, Australia IEEE Neural Networks Soc. Univ. of New South Wales |
| Starting Page | 443 |
| Ending Page | 450 |
| File Size | 622446 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780378040 |
| DOI | 10.1109/CEC.2003.1299609 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-12-08 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Genetic mutations Space technology Evolutionary computation Information technology Australia Histograms DC generators Genetic algorithms Genetic programming Concurrent computing |
| Content Type | Text |
| Resource Type | Article |
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